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Facial Expression Recognition Using Soft Computing

Khademi, Mahmoud | 2009

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 39525 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Manzuri, Mohammad Taghi
  7. Abstract:
  8. Human face-to-face communication is an ideal model for designing a multimodal/media human-computer interface (HCI). Recent advances in image analysis and pattern recognition open up the possibility of automatic detection and classification of emotional and conversational facial signals. Automating facial expression analysis could bring facial expressions into man-machine interaction as a new modality and make the interaction tighter and more efficient. In this research an accurate real-time sequence-based system for representation, recognition and analysis of low-intensity facial expressions and facial action uints (FAUs) is presented. The feature extraction is done using facial feature point tracking and Kernel Baised Discriminant Analysis (KBDA) as an efficient dimension reduction method. A novel classification scheme based on neuro-fuzyy modeling of the FAU intensity is also presented. In this method, Takagi-Sugeno type ANFIS is used to extract the fuzzy rules. Each rule applies a linear approximation to estimate the FAU intensity in a specific fuzzy subspace. In comparison with the SVMs, the ability of this method to model a highly non-linear system and the fuzzy natrual of the FAUs causes high recognition rate of the low intensity and combined FAUs, on Cohn-Kanade database. Moreover, the rule-based classifiers are used for classification of the six basic facial expressions. These classifiers use the FAU intensity as a feature with continous value. Thus, novel accurate models for facial expression interpertation by FAUs, based on rule-based classifiers (such as Neuro-fuzzy decision trees), can be created. These models would be useful in animation and cognitive and behavioral sciences areas
  9. Keywords:
  10. Support Vector Machine (SVM) ; Facial Expression Recognition ; Feature Point Tracking ; Baised Discriminant Analysis ; Adaptive-Network-Based Fuzzy Inference System

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